%0 Journal Article
%@ 1742-1772
%A Piribauer, P.
%A Crespo Cuaresma, J.
%D 2016
%F iiasa:13930
%I Routledge
%J Spatial Economic Analysis
%K determinants of economic growth; Markov chain Monte Carlo methods; model uncertainty; Spatial autoregressive model; variable selection
%N 4
%P 457-479
%R 10.1080/17421772.2016.1227468
%T Bayesian Variable Selection in Spatial Autoregressive Models
%U https://pure.iiasa.ac.at/id/eprint/13930/
%V 11
%X This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. It presents two alternative approaches that can be implemented using Gibbs sampling methods in a straightforward way and which allow one to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. A simulation study shows that the variable selection approaches tend to outperform existing Bayesian model averaging techniques in terms of both in-sample predictive performance and computational efficiency. The alternative approaches are compared in an empirical application using data on economic growth for European NUTS-2 regions.